Comparison of the Sampling Efficiency in Spatial Autoregressive Model

Yoshihiro Ohtsuka¹, Kazuhiko Kakamu²

¹Department of Economics, University of Nagasaki, Nagasaki, Japan.
²Faculty of Law, Politics and Economics, Chiba University, Chiba, Japan.

DOI: 10.4236/ojs.2015.51002                

Abstract

A random walk Metropolis-Hastings algorithm has been widely used in sampling the parameter of spatial interaction in spatial autoregressive model from a Bayesian point of view. In addition, as an alternative approach, the griddy Gibbs sampler is proposed by [1] and utilized by [2]. This paper proposes an acceptance-rejection Metropolis-Hastings algorithm as a third approach, and compares these three algorithms through Monte Carlo experiments. The experimental results show that the griddy Gibbs sampler is the most efficient algorithm among the algorithms whether the number of observations is small or not in terms of the computation time and the inefficiency factors. Moreover, it seems to work well when the size of grid is 100.

Keywords

Acceptance-Rejection Metropolis-Hastings Algorithm, Griddy Gibbs Sampler, Markov Chain Monte Carlo (MCMC), Random Walk Metropolis-Hastings Algorithm, Spatial Autoregressive Model

Conflicts of Interest

The authors declare no conflicts of interest.

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